{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4a801e3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num</th>\n",
       "      <th>merchant_profit</th>\n",
       "      <th>customer_spending</th>\n",
       "      <th>customer_savings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>25.25</td>\n",
       "      <td>74.25</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>49.00</td>\n",
       "      <td>147.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>71.25</td>\n",
       "      <td>218.25</td>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>92.00</td>\n",
       "      <td>288.00</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>111.25</td>\n",
       "      <td>356.25</td>\n",
       "      <td>18.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>129.00</td>\n",
       "      <td>423.00</td>\n",
       "      <td>27.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>145.25</td>\n",
       "      <td>488.25</td>\n",
       "      <td>36.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>160.00</td>\n",
       "      <td>552.00</td>\n",
       "      <td>48.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>173.25</td>\n",
       "      <td>614.25</td>\n",
       "      <td>60.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>185.00</td>\n",
       "      <td>675.00</td>\n",
       "      <td>75.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>195.25</td>\n",
       "      <td>734.25</td>\n",
       "      <td>90.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>204.00</td>\n",
       "      <td>792.00</td>\n",
       "      <td>108.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>211.25</td>\n",
       "      <td>848.25</td>\n",
       "      <td>126.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>217.00</td>\n",
       "      <td>903.00</td>\n",
       "      <td>147.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>221.25</td>\n",
       "      <td>956.25</td>\n",
       "      <td>168.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>224.00</td>\n",
       "      <td>1008.00</td>\n",
       "      <td>192.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>225.25</td>\n",
       "      <td>1058.25</td>\n",
       "      <td>216.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>225.00</td>\n",
       "      <td>1107.00</td>\n",
       "      <td>243.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>223.25</td>\n",
       "      <td>1154.25</td>\n",
       "      <td>270.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>220.00</td>\n",
       "      <td>1200.00</td>\n",
       "      <td>300.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>215.25</td>\n",
       "      <td>1244.25</td>\n",
       "      <td>330.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>209.00</td>\n",
       "      <td>1287.00</td>\n",
       "      <td>363.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>201.25</td>\n",
       "      <td>1328.25</td>\n",
       "      <td>396.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>192.00</td>\n",
       "      <td>1368.00</td>\n",
       "      <td>432.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>181.25</td>\n",
       "      <td>1406.25</td>\n",
       "      <td>468.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>169.00</td>\n",
       "      <td>1443.00</td>\n",
       "      <td>507.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>155.25</td>\n",
       "      <td>1478.25</td>\n",
       "      <td>546.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>140.00</td>\n",
       "      <td>1512.00</td>\n",
       "      <td>588.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>123.25</td>\n",
       "      <td>1544.25</td>\n",
       "      <td>630.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>105.00</td>\n",
       "      <td>1575.00</td>\n",
       "      <td>675.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    num  merchant_profit  customer_spending  customer_savings\n",
       "0     1            25.25              74.25              0.75\n",
       "1     2            49.00             147.00              3.00\n",
       "2     3            71.25             218.25              6.75\n",
       "3     4            92.00             288.00             12.00\n",
       "4     5           111.25             356.25             18.75\n",
       "5     6           129.00             423.00             27.00\n",
       "6     7           145.25             488.25             36.75\n",
       "7     8           160.00             552.00             48.00\n",
       "8     9           173.25             614.25             60.75\n",
       "9    10           185.00             675.00             75.00\n",
       "10   11           195.25             734.25             90.75\n",
       "11   12           204.00             792.00            108.00\n",
       "12   13           211.25             848.25            126.75\n",
       "13   14           217.00             903.00            147.00\n",
       "14   15           221.25             956.25            168.75\n",
       "15   16           224.00            1008.00            192.00\n",
       "16   17           225.25            1058.25            216.75\n",
       "17   18           225.00            1107.00            243.00\n",
       "18   19           223.25            1154.25            270.75\n",
       "19   20           220.00            1200.00            300.00\n",
       "20   21           215.25            1244.25            330.75\n",
       "21   22           209.00            1287.00            363.00\n",
       "22   23           201.25            1328.25            396.75\n",
       "23   24           192.00            1368.00            432.00\n",
       "24   25           181.25            1406.25            468.75\n",
       "25   26           169.00            1443.00            507.00\n",
       "26   27           155.25            1478.25            546.75\n",
       "27   28           140.00            1512.00            588.00\n",
       "28   29           123.25            1544.25            630.75\n",
       "29   30           105.00            1575.00            675.00"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#自备数据源，读取操作\n",
    "import pandas as pd\n",
    "data = pd.read_csv(\"./data/data_sales.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7eb6665c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      2\n",
       "2      3\n",
       "3      4\n",
       "4      5\n",
       "5      6\n",
       "6      7\n",
       "7      8\n",
       "8      9\n",
       "9     10\n",
       "10    11\n",
       "11    12\n",
       "12    13\n",
       "13    14\n",
       "14    15\n",
       "15    16\n",
       "16    17\n",
       "17    18\n",
       "18    19\n",
       "19    20\n",
       "20    21\n",
       "21    22\n",
       "22    23\n",
       "23    24\n",
       "24    25\n",
       "25    26\n",
       "26    27\n",
       "27    28\n",
       "28    29\n",
       "29    30\n",
       "Name: num, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#顾客购买数量列表\n",
    "num_items = data['num']\n",
    "num_items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3ef77fe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      25.25\n",
       "1      49.00\n",
       "2      71.25\n",
       "3      92.00\n",
       "4     111.25\n",
       "5     129.00\n",
       "6     145.25\n",
       "7     160.00\n",
       "8     173.25\n",
       "9     185.00\n",
       "10    195.25\n",
       "11    204.00\n",
       "12    211.25\n",
       "13    217.00\n",
       "14    221.25\n",
       "15    224.00\n",
       "16    225.25\n",
       "17    225.00\n",
       "18    223.25\n",
       "19    220.00\n",
       "20    215.25\n",
       "21    209.00\n",
       "22    201.25\n",
       "23    192.00\n",
       "24    181.25\n",
       "25    169.00\n",
       "26    155.25\n",
       "27    140.00\n",
       "28    123.25\n",
       "29    105.00\n",
       "Name: merchant_profit, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#商家收益列表\n",
    "merchant_profit = data['merchant_profit']\n",
    "merchant_profit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dbc9ef2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       74.25\n",
       "1      147.00\n",
       "2      218.25\n",
       "3      288.00\n",
       "4      356.25\n",
       "5      423.00\n",
       "6      488.25\n",
       "7      552.00\n",
       "8      614.25\n",
       "9      675.00\n",
       "10     734.25\n",
       "11     792.00\n",
       "12     848.25\n",
       "13     903.00\n",
       "14     956.25\n",
       "15    1008.00\n",
       "16    1058.25\n",
       "17    1107.00\n",
       "18    1154.25\n",
       "19    1200.00\n",
       "20    1244.25\n",
       "21    1287.00\n",
       "22    1328.25\n",
       "23    1368.00\n",
       "24    1406.25\n",
       "25    1443.00\n",
       "26    1478.25\n",
       "27    1512.00\n",
       "28    1544.25\n",
       "29    1575.00\n",
       "Name: customer_spending, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#顾客总消费列表\n",
    "customer_spending = data['customer_spending']\n",
    "customer_spending"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0d029692",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.75\n",
       "1       3.00\n",
       "2       6.75\n",
       "3      12.00\n",
       "4      18.75\n",
       "5      27.00\n",
       "6      36.75\n",
       "7      48.00\n",
       "8      60.75\n",
       "9      75.00\n",
       "10     90.75\n",
       "11    108.00\n",
       "12    126.75\n",
       "13    147.00\n",
       "14    168.75\n",
       "15    192.00\n",
       "16    216.75\n",
       "17    243.00\n",
       "18    270.75\n",
       "19    300.00\n",
       "20    330.75\n",
       "21    363.00\n",
       "22    396.75\n",
       "23    432.00\n",
       "24    468.75\n",
       "25    507.00\n",
       "26    546.75\n",
       "27    588.00\n",
       "28    630.75\n",
       "29    675.00\n",
       "Name: customer_savings, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#顾客省钱情况列表\n",
    "customer_savings = data['customer_savings']\n",
    "customer_savings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3e7c5a0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#设置中文显示\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "plt.plot(num_items,merchant_profit,label='Merchant Profit',marker='o')\n",
    "plt.plot(num_items,customer_spending,label='Customer Spending',marker='o')\n",
    "plt.plot(num_items,customer_savings,label='Customer Saving',marker='o')\n",
    "plt.xlabel(\"商家购买数量\")\n",
    "plt.ylabel(\"价格/元\")\n",
    "plt.title(\"\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a496f401",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
